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| 贝叶斯零膨胀模型× | 贝叶斯泊松回归× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1992–2006 | 1989 (GLM foundation); Bayesian treatment formalized in 1990s–2000s |
| 提出者≠ | Lambert (1992) for ZIP; Bayesian extension by Ghosh, Mukhopadhyay & Lu (2006) | Gelman et al. (BDA); classical Poisson GLM from McCullagh & Nelder (1989) |
| 类型≠ | Bayesian count regression | Bayesian generalized linear model for count data |
| 开创性文献≠ | Ghosh, S. K., Mukhopadhyay, P., & Lu, J.-C. (2006). Bayesian analysis of zero-inflated regression models. Journal of Statistical Planning and Inference, 136(4), 1360–1375. DOI ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| 别名 | Bayesian ZIP, Bayesian ZINB, Bayesian zero-inflated Poisson, Bayesian zero-inflated negative binomial | Bayesian log-linear count model, Bayesian GLM Poisson, Poisson regression with priors, Bayesian count regression |
| 相关≠ | 5 | 6 |
| 摘要≠ | The Bayesian zero-inflated model handles count data with excess zeros by combining a binary component — identifying structural zeros — with a count component (Poisson or negative binomial) for the remaining counts. Bayesian inference via MCMC provides full posterior distributions for all parameters, enabling principled uncertainty quantification and regularisation through priors. | Bayesian Poisson regression models non-negative integer count outcomes using a Poisson likelihood with a log link, placing prior distributions on the regression coefficients. Posterior inference — combining prior beliefs with the data likelihood — produces full probability distributions over the coefficients rather than single-point estimates, enabling coherent uncertainty quantification and incorporation of domain knowledge. |
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